{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***ALD Random Forest***\n",
    "**Anthony DiBenedetto**\n",
    "\n",
    "This program uses a random forest to comapre the accuracy of the model vs the amount of data used to create the model. I perform this in 4 different ways:\n",
    "- Remove the middle middle channel of 40 dimensions\n",
    "- Remove every other column\n",
    "- Separate the 120 columns into the 3 channels and remove 1 column from the channels\n",
    "- Remove the last element from the 120 columns\n",
    "\n",
    "After each of these tests there is a graph or confusion matrix to help visualise the results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the training and test data\n",
    "training_data = (np.load('ALD_Classification_dataset/training_data.npy'))\n",
    "training_labels = (np.load('ALD_Classification_dataset/training_labels.npy'))\n",
    "\n",
    "testing_data = (np.load('ALD_Classification_dataset/testing_data.npy'))\n",
    "testing_labels = (np.load('ALD_Classification_dataset/testing_labels.npy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>999</th>\n",
       "      <td>0</td>\n",
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       "<p>1000 rows × 1 columns</p>\n",
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       "     0\n",
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       "\n",
       "[1000 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(testing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "pd.DataFrame(testing_labels).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = training_data\n",
    "y_train = training_labels\n",
    "\n",
    "X_test = testing_data\n",
    "y_test = testing_labels"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Remove middle channel of 40 dimensions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_0 = X_train \n",
    "y_train_0 = y_train\n",
    "\n",
    "X_test_0 = X_test\n",
    "y_test_0 = y_test\n",
    "\n",
    "# Remove middle 40 columns\n",
    "\n",
    "X_train_0 = np.delete(X_train_0, np.s_[40:80], axis=1)\n",
    "X_test_0 = np.delete(X_test_0, np.s_[40:80], axis=1)\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators=250, max_depth=25, max_features='sqrt', class_weight= \"balanced\" , random_state=42)\n",
    "rfc.fit(X_train_0, y_train_0)\n",
    "\n",
    "y_pred = rfc.predict(X_test_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 98.80%\n",
      "Confusion matrix:\n",
      " [[612   7]\n",
      " [  5 376]]\n"
     ]
    }
   ],
   "source": [
    "# Generate the accuracy from the predicted y values (y_pred) and y_test \n",
    "accuracy = accuracy_score(y_test_0, y_pred)\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy*100))\n",
    "\n",
    "# Generate the confusion matrix from predicted y values (y_pred) and y_test \n",
    "cm = confusion_matrix(y_test_0, y_pred)\n",
    "print(\"Confusion matrix:\\n\", cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use seaborn package to make the confusion matrix look nice, as well as normilize the confusion matrix\n",
    "import seaborn as sns\n",
    "cm_normalized = np.round(cm/np.sum(cm, axis=1).reshape(-1, 1), 2) \n",
    "sns.heatmap(cm_normalized, cmap=\"Greens\", annot=True)\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"Actual Label\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Remove every other column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.99 | Number of Columns: 120\n",
      "Accuracy: 0.986 | Number of Columns: 60\n",
      "Accuracy: 0.987 | Number of Columns: 30\n",
      "Accuracy: 0.988 | Number of Columns: 15\n",
      "Accuracy: 0.987 | Number of Columns: 8\n",
      "Accuracy: 0.988 | Number of Columns: 4\n",
      "Accuracy: 0.969 | Number of Columns: 2\n",
      "Accuracy: 0.759 | Number of Columns: 1\n"
     ]
    }
   ],
   "source": [
    "X_train_1 = X_train \n",
    "y_train_1 = y_train\n",
    "\n",
    "X_test_1 = X_test\n",
    "y_test_1 = y_test\n",
    "\n",
    "# Every Other Column Removal\n",
    "accuracy_list = []\n",
    "columns = []\n",
    "\n",
    "all_data = {}\n",
    "\n",
    "for i in range(8):\n",
    "    \n",
    "    rfc = RandomForestClassifier(n_estimators=250, max_depth=25, max_features='sqrt', class_weight= \"balanced\" , random_state=42)\n",
    "    rfc.fit(X_train_1, y_train_1)\n",
    "\n",
    "    y_pred = rfc.predict(X_test_1)\n",
    "    accuracy = accuracy_score(y_test_1, y_pred)\n",
    "    accuracy_list.append(accuracy)   \n",
    "\n",
    "    columns.append(X_train_1.shape[1])\n",
    "    print(\"Accuracy: \" + str(accuracy) + \" | Number of Columns: \" + str(X_train_1.shape[1]))\n",
    "    all_data[X_train_1.shape[1]] = [X_train_1, X_test_1]\n",
    "\n",
    "    X_train_1 = pd.DataFrame(X_train_1).iloc[:, ::2] \n",
    "\n",
    "    X_test_1 = pd.DataFrame(X_test_1).iloc[:, ::2]\n",
    "\n",
    "    X_train_1 = X_train_1.values\n",
    "\n",
    "    X_test_1 = X_test_1.values\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.356059</td>\n",
       "      <td>0.336917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.788309</td>\n",
       "      <td>0.008476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.281496</td>\n",
       "      <td>0.290075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.201490</td>\n",
       "      <td>0.232997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.462808</td>\n",
       "      <td>0.248565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1\n",
       "0  0.356059  0.336917\n",
       "1  0.788309  0.008476\n",
       "2  0.281496  0.290075\n",
       "3  0.201490  0.232997\n",
       "4  0.462808  0.248565"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since 2 columns was still a pretty high accuacy lets look at the Train and Test data\n",
    "\n",
    "# Train data\n",
    "pd.DataFrame(all_data[2][0]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.239797</td>\n",
       "      <td>0.263403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.305793</td>\n",
       "      <td>0.339097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.385707</td>\n",
       "      <td>0.139171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.974438</td>\n",
       "      <td>0.015819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.297728</td>\n",
       "      <td>0.283998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1\n",
       "0  0.239797  0.263403\n",
       "1  0.305793  0.339097\n",
       "2  0.385707  0.139171\n",
       "3  0.974438  0.015819\n",
       "4  0.297728  0.283998"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test data\n",
    "pd.DataFrame(all_data[2][1]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 6))\n",
    "\n",
    "columns = columns[::-1]\n",
    "accuracy_list = accuracy_list[::-1]\n",
    "\n",
    "# evenly space the columns using np.linspace\n",
    "new_columns = np.linspace(min(columns), max(columns), len(columns))\n",
    "\n",
    "plt.plot(new_columns, accuracy_list, color='blue', linestyle='--', marker='o', linewidth=2, label='Accuracy')\n",
    "plt.xlabel('Number of Columns', fontsize=14)\n",
    "plt.ylabel('Accuracy', fontsize=14)\n",
    "plt.title('Accuracy vs Number of Columns', fontsize=16)\n",
    "\n",
    "# Reverse the order of the tick labels and set them at an angle\n",
    "plt.xticks(new_columns, columns, rotation=45, ha='right', fontsize=12)\n",
    "plt.gca().invert_xaxis()  # invert the x-axis to keep the tick labels in order\n",
    "\n",
    "plt.yticks(fontsize=12)\n",
    "plt.grid()\n",
    "plt.legend(loc='lower left')\n",
    "\n",
    "plt.xlim(max(columns), min(columns))\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Separate the 120 columns into the 3 channels and remove 1 column from the channels**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.99 | Number of Columns: 120\n",
      "Accuracy: 0.989 | Number of Columns: 117\n",
      "Accuracy: 0.989 | Number of Columns: 114\n",
      "Accuracy: 0.988 | Number of Columns: 111\n",
      "Accuracy: 0.989 | Number of Columns: 108\n",
      "Accuracy: 0.989 | Number of Columns: 105\n",
      "Accuracy: 0.989 | Number of Columns: 102\n",
      "Accuracy: 0.989 | Number of Columns: 99\n",
      "Accuracy: 0.991 | Number of Columns: 96\n",
      "Accuracy: 0.988 | Number of Columns: 93\n",
      "Accuracy: 0.989 | Number of Columns: 90\n",
      "Accuracy: 0.989 | Number of Columns: 87\n",
      "Accuracy: 0.988 | Number of Columns: 84\n",
      "Accuracy: 0.989 | Number of Columns: 81\n",
      "Accuracy: 0.99 | Number of Columns: 78\n",
      "Accuracy: 0.989 | Number of Columns: 75\n",
      "Accuracy: 0.988 | Number of Columns: 72\n",
      "Accuracy: 0.99 | Number of Columns: 69\n",
      "Accuracy: 0.987 | Number of Columns: 66\n",
      "Accuracy: 0.989 | Number of Columns: 63\n",
      "Accuracy: 0.99 | Number of Columns: 60\n",
      "Accuracy: 0.989 | Number of Columns: 57\n",
      "Accuracy: 0.991 | Number of Columns: 54\n",
      "Accuracy: 0.99 | Number of Columns: 51\n",
      "Accuracy: 0.991 | Number of Columns: 48\n",
      "Accuracy: 0.989 | Number of Columns: 45\n",
      "Accuracy: 0.99 | Number of Columns: 42\n",
      "Accuracy: 0.988 | Number of Columns: 39\n",
      "Accuracy: 0.987 | Number of Columns: 36\n",
      "Accuracy: 0.986 | Number of Columns: 33\n",
      "Accuracy: 0.987 | Number of Columns: 30\n",
      "Accuracy: 0.987 | Number of Columns: 27\n",
      "Accuracy: 0.985 | Number of Columns: 24\n",
      "Accuracy: 0.987 | Number of Columns: 21\n",
      "Accuracy: 0.986 | Number of Columns: 18\n",
      "Accuracy: 0.987 | Number of Columns: 15\n",
      "Accuracy: 0.985 | Number of Columns: 12\n",
      "Accuracy: 0.983 | Number of Columns: 9\n",
      "Accuracy: 0.979 | Number of Columns: 6\n",
      "Accuracy: 0.97 | Number of Columns: 3\n"
     ]
    }
   ],
   "source": [
    "X_train_2 = X_train \n",
    "y_train_2 = y_train\n",
    "\n",
    "X_test_2 = X_test\n",
    "y_test_2 = y_test\n",
    "\n",
    "#Separates 120 columns into 3 arrays of 40 colums and the removes one row from each array and displays its accuracy\n",
    "accuracy_list = []\n",
    "columns = []\n",
    "\n",
    "all_data = {}\n",
    "\n",
    "\n",
    "train_arr1, train_arr2, train_arr3 = np.split(X_train_2, 3, axis=1)\n",
    "test_arr1, test_arr2, test_arr3 = np.split(X_test_2, 3, axis=1)\n",
    "\n",
    "combined_arr_train = np.concatenate((train_arr1, train_arr2, train_arr3), axis=1)\n",
    "\n",
    "\n",
    "while combined_arr_train.shape[1] > 3:\n",
    "    \n",
    "    rfc = RandomForestClassifier(n_estimators=250, max_depth=25, max_features='sqrt', class_weight= \"balanced\" , random_state=42)\n",
    "    \n",
    "    combined_arr_train = np.concatenate((train_arr1, train_arr2, train_arr3), axis=1)\n",
    "    combined_arr_test = np.concatenate((test_arr1, test_arr2, test_arr3), axis=1)\n",
    "\n",
    "    rfc.fit(combined_arr_train, y_train_2)\n",
    "\n",
    "    y_pred = rfc.predict(combined_arr_test)\n",
    "    accuracy = accuracy_score(y_test_2, y_pred)\n",
    "    accuracy_list.append(accuracy)   \n",
    "\n",
    "    columns.append(combined_arr_train.shape[1])\n",
    "    print(\"Accuracy: \" + str(accuracy) + \" | Number of Columns: \" + str(combined_arr_train.shape[1]))\n",
    "    all_data[combined_arr_train.shape[1]] = [combined_arr_train, combined_arr_test]\n",
    " \n",
    "    train_arr1 =np.delete(train_arr1, -1, axis=1)\n",
    "\n",
    "    train_arr2 =np.delete(train_arr2, -1, axis=1)\n",
    "\n",
    "    train_arr3 =np.delete(train_arr3, -1, axis=1)\n",
    "\n",
    "    test_arr1 =np.delete(test_arr1, -1, axis=1)\n",
    "\n",
    "    test_arr2 =np.delete(test_arr2, -1, axis=1)\n",
    "\n",
    "    test_arr3 =np.delete(test_arr3, -1, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.991"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets get the max accuracy from that loop\n",
    "max(accuracy_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96\n",
      "54\n",
      "48\n"
     ]
    }
   ],
   "source": [
    "# Lets see how many colums that is \n",
    "max_value = max(accuracy_list)\n",
    "max_indexes = [i for i, value in enumerate(accuracy_list) if value == max_value]\n",
    "\n",
    "for index in max_indexes:\n",
    "    print(columns[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Each numpy array had 16 column\n",
    "48 / 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18, 6))\n",
    "\n",
    "columns = columns[::-1]\n",
    "accuracy_list = accuracy_list[::-1]\n",
    "\n",
    "# evenly space the columns using np.linspace\n",
    "new_columns = np.linspace(min(columns), max(columns), len(columns))\n",
    "\n",
    "plt.plot(new_columns, accuracy_list, color='blue', linestyle='--', marker='o', linewidth=2, label='Accuracy')\n",
    "plt.xlabel('Number of Columns', fontsize=14)\n",
    "plt.ylabel('Accuracy', fontsize=14)\n",
    "plt.title('Accuracy vs Number of Columns', fontsize=16)\n",
    "\n",
    "# Reverse the order of the tick labels and set them at an angle\n",
    "plt.xticks(new_columns, columns, rotation=45, ha='right', fontsize=12)\n",
    "plt.gca().invert_xaxis()  # invert the x-axis to keep the tick labels in order\n",
    "\n",
    "plt.yticks(fontsize=12)\n",
    "plt.grid()\n",
    "plt.legend(loc='lower left')\n",
    "\n",
    "plt.xlim(max(columns), min(columns))\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Remove Last column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.99 | Number of Columns: 120\n",
      "Accuracy: 0.99 | Number of Columns: 119\n",
      "Accuracy: 0.99 | Number of Columns: 118\n",
      "Accuracy: 0.989 | Number of Columns: 117\n",
      "Accuracy: 0.989 | Number of Columns: 116\n",
      "Accuracy: 0.989 | Number of Columns: 115\n",
      "Accuracy: 0.989 | Number of Columns: 114\n",
      "Accuracy: 0.988 | Number of Columns: 113\n",
      "Accuracy: 0.989 | Number of Columns: 112\n",
      "Accuracy: 0.988 | Number of Columns: 111\n",
      "Accuracy: 0.989 | Number of Columns: 110\n",
      "Accuracy: 0.989 | Number of Columns: 109\n",
      "Accuracy: 0.989 | Number of Columns: 108\n",
      "Accuracy: 0.99 | Number of Columns: 107\n",
      "Accuracy: 0.988 | Number of Columns: 106\n",
      "Accuracy: 0.99 | Number of Columns: 105\n",
      "Accuracy: 0.989 | Number of Columns: 104\n",
      "Accuracy: 0.989 | Number of Columns: 103\n",
      "Accuracy: 0.989 | Number of Columns: 102\n",
      "Accuracy: 0.989 | Number of Columns: 101\n",
      "Accuracy: 0.99 | Number of Columns: 100\n",
      "Accuracy: 0.991 | Number of Columns: 99\n",
      "Accuracy: 0.99 | Number of Columns: 98\n",
      "Accuracy: 0.991 | Number of Columns: 97\n",
      "Accuracy: 0.991 | Number of Columns: 96\n",
      "Accuracy: 0.99 | Number of Columns: 95\n",
      "Accuracy: 0.991 | Number of Columns: 94\n",
      "Accuracy: 0.989 | Number of Columns: 93\n",
      "Accuracy: 0.99 | Number of Columns: 92\n",
      "Accuracy: 0.991 | Number of Columns: 91\n",
      "Accuracy: 0.991 | Number of Columns: 90\n",
      "Accuracy: 0.991 | Number of Columns: 89\n",
      "Accuracy: 0.988 | Number of Columns: 88\n",
      "Accuracy: 0.991 | Number of Columns: 87\n",
      "Accuracy: 0.988 | Number of Columns: 86\n",
      "Accuracy: 0.991 | Number of Columns: 85\n",
      "Accuracy: 0.99 | Number of Columns: 84\n",
      "Accuracy: 0.99 | Number of Columns: 83\n",
      "Accuracy: 0.99 | Number of Columns: 82\n",
      "Accuracy: 0.989 | Number of Columns: 81\n",
      "Accuracy: 0.987 | Number of Columns: 80\n",
      "Accuracy: 0.988 | Number of Columns: 79\n",
      "Accuracy: 0.988 | Number of Columns: 78\n",
      "Accuracy: 0.988 | Number of Columns: 77\n",
      "Accuracy: 0.988 | Number of Columns: 76\n",
      "Accuracy: 0.987 | Number of Columns: 75\n",
      "Accuracy: 0.988 | Number of Columns: 74\n",
      "Accuracy: 0.989 | Number of Columns: 73\n",
      "Accuracy: 0.988 | Number of Columns: 72\n",
      "Accuracy: 0.988 | Number of Columns: 71\n",
      "Accuracy: 0.988 | Number of Columns: 70\n",
      "Accuracy: 0.986 | Number of Columns: 69\n",
      "Accuracy: 0.988 | Number of Columns: 68\n",
      "Accuracy: 0.989 | Number of Columns: 67\n",
      "Accuracy: 0.987 | Number of Columns: 66\n",
      "Accuracy: 0.986 | Number of Columns: 65\n",
      "Accuracy: 0.988 | Number of Columns: 64\n",
      "Accuracy: 0.988 | Number of Columns: 63\n",
      "Accuracy: 0.987 | Number of Columns: 62\n",
      "Accuracy: 0.989 | Number of Columns: 61\n",
      "Accuracy: 0.987 | Number of Columns: 60\n",
      "Accuracy: 0.99 | Number of Columns: 59\n",
      "Accuracy: 0.99 | Number of Columns: 58\n",
      "Accuracy: 0.988 | Number of Columns: 57\n",
      "Accuracy: 0.989 | Number of Columns: 56\n",
      "Accuracy: 0.988 | Number of Columns: 55\n",
      "Accuracy: 0.988 | Number of Columns: 54\n",
      "Accuracy: 0.987 | Number of Columns: 53\n",
      "Accuracy: 0.987 | Number of Columns: 52\n",
      "Accuracy: 0.988 | Number of Columns: 51\n",
      "Accuracy: 0.986 | Number of Columns: 50\n",
      "Accuracy: 0.987 | Number of Columns: 49\n",
      "Accuracy: 0.988 | Number of Columns: 48\n",
      "Accuracy: 0.989 | Number of Columns: 47\n",
      "Accuracy: 0.987 | Number of Columns: 46\n",
      "Accuracy: 0.986 | Number of Columns: 45\n",
      "Accuracy: 0.987 | Number of Columns: 44\n",
      "Accuracy: 0.986 | Number of Columns: 43\n",
      "Accuracy: 0.985 | Number of Columns: 42\n",
      "Accuracy: 0.985 | Number of Columns: 41\n",
      "Accuracy: 0.986 | Number of Columns: 40\n",
      "Accuracy: 0.984 | Number of Columns: 39\n",
      "Accuracy: 0.984 | Number of Columns: 38\n",
      "Accuracy: 0.984 | Number of Columns: 37\n",
      "Accuracy: 0.985 | Number of Columns: 36\n",
      "Accuracy: 0.983 | Number of Columns: 35\n",
      "Accuracy: 0.982 | Number of Columns: 34\n",
      "Accuracy: 0.988 | Number of Columns: 33\n",
      "Accuracy: 0.984 | Number of Columns: 32\n",
      "Accuracy: 0.986 | Number of Columns: 31\n",
      "Accuracy: 0.984 | Number of Columns: 30\n",
      "Accuracy: 0.983 | Number of Columns: 29\n",
      "Accuracy: 0.981 | Number of Columns: 28\n",
      "Accuracy: 0.983 | Number of Columns: 27\n",
      "Accuracy: 0.987 | Number of Columns: 26\n",
      "Accuracy: 0.981 | Number of Columns: 25\n",
      "Accuracy: 0.984 | Number of Columns: 24\n",
      "Accuracy: 0.982 | Number of Columns: 23\n",
      "Accuracy: 0.982 | Number of Columns: 22\n",
      "Accuracy: 0.98 | Number of Columns: 21\n",
      "Accuracy: 0.979 | Number of Columns: 20\n",
      "Accuracy: 0.977 | Number of Columns: 19\n",
      "Accuracy: 0.978 | Number of Columns: 18\n",
      "Accuracy: 0.979 | Number of Columns: 17\n",
      "Accuracy: 0.977 | Number of Columns: 16\n",
      "Accuracy: 0.977 | Number of Columns: 15\n",
      "Accuracy: 0.975 | Number of Columns: 14\n",
      "Accuracy: 0.973 | Number of Columns: 13\n",
      "Accuracy: 0.97 | Number of Columns: 12\n",
      "Accuracy: 0.969 | Number of Columns: 11\n",
      "Accuracy: 0.97 | Number of Columns: 10\n",
      "Accuracy: 0.97 | Number of Columns: 9\n",
      "Accuracy: 0.971 | Number of Columns: 8\n",
      "Accuracy: 0.966 | Number of Columns: 7\n",
      "Accuracy: 0.964 | Number of Columns: 6\n",
      "Accuracy: 0.955 | Number of Columns: 5\n",
      "Accuracy: 0.957 | Number of Columns: 4\n"
     ]
    }
   ],
   "source": [
    "X_train_3 = X_train \n",
    "y_train_3 = y_train\n",
    "\n",
    "X_test_3 = X_test\n",
    "y_test_3 = y_test\n",
    "\n",
    "# Remove last column\n",
    "accuracy_list = []\n",
    "columns = []\n",
    "\n",
    "all_data = {}\n",
    "\n",
    "while X_train_3.shape[1] > 3:\n",
    "    \n",
    "    rfc = RandomForestClassifier(n_estimators=250, max_depth=25, max_features='sqrt', class_weight= \"balanced\" , random_state=42)\n",
    "    rfc.fit(X_train_3, y_train_3)\n",
    "\n",
    "    y_pred = rfc.predict(X_test_3)\n",
    "    accuracy = accuracy_score(y_test_3, y_pred)\n",
    "    accuracy_list.append(accuracy)   \n",
    "\n",
    "    columns.append(X_train_3.shape[1])\n",
    "    print(\"Accuracy: \" + str(accuracy) + \" | Number of Columns: \" + str(X_train_3.shape[1]))\n",
    "    all_data[X_train_3.shape[1]] = [X_train_3, X_test_3]\n",
    "\n",
    "    X_train_3 = np.delete(X_train_3, -1, axis=1)\n",
    "\n",
    "    X_test_3 = np.delete(X_test_3, -1, axis=1)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.991"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets get the max accuracy from that loop\n",
    "max(accuracy_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99\n",
      "97\n",
      "96\n",
      "94\n",
      "91\n",
      "90\n",
      "89\n",
      "87\n",
      "85\n"
     ]
    }
   ],
   "source": [
    "# All colums that are equal with the max\n",
    "max_value = max(accuracy_list)\n",
    "max_indexes = [i for i, value in enumerate(accuracy_list) if value == max_value]\n",
    "\n",
    "for index in max_indexes:\n",
    "    print(columns[index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2520x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(35, 17))\n",
    "\n",
    "columns = columns[::-1]\n",
    "accuracy_list = accuracy_list[::-1]\n",
    "\n",
    "# evenly space the columns using np.linspace\n",
    "new_columns = np.linspace(min(columns), max(columns), len(columns))\n",
    "\n",
    "plt.plot(new_columns, accuracy_list, color='blue', linestyle='--', marker='o', linewidth=2, label='Accuracy')\n",
    "plt.xlabel('Number of Columns', fontsize=14)\n",
    "plt.ylabel('Accuracy', fontsize=14)\n",
    "plt.title('Accuracy vs Number of Columns', fontsize=16)\n",
    "\n",
    "# Reverse the order of the tick labels and set them at an angle\n",
    "plt.xticks(new_columns, columns, rotation=45, ha='right', fontsize=12)\n",
    "plt.gca().invert_xaxis()  # invert the x-axis to keep the tick labels in order\n",
    "\n",
    "plt.yticks(fontsize=12)\n",
    "plt.grid()\n",
    "plt.legend(loc='lower left')\n",
    "\n",
    "plt.xlim(min(columns), max(columns))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "11f5c4aeecadc3e88c152309abda6ed29843603433e3c3abe7ec8b133e6a3d72"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
